3,689 research outputs found
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation
In the traditional object recognition pipeline, descriptors are densely
sampled over an image, pooled into a high dimensional non-linear representation
and then passed to a classifier. In recent years, Fisher Vectors have proven
empirically to be the leading representation for a large variety of
applications. The Fisher Vector is typically taken as the gradients of the
log-likelihood of descriptors, with respect to the parameters of a Gaussian
Mixture Model (GMM). Motivated by the assumption that different distributions
should be applied for different datasets, we present two other Mixture Models
and derive their Expectation-Maximization and Fisher Vector expressions. The
first is a Laplacian Mixture Model (LMM), which is based on the Laplacian
distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian
Mixture Model (HGLMM) which is based on a weighted geometric mean of the
Gaussian and Laplacian distribution. An interesting property of the
Expectation-Maximization algorithm for the latter is that in the maximization
step, each dimension in each component is chosen to be either a Gaussian or a
Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we
achieve state-of-the-art results for both the image annotation and the image
search by a sentence tasks.Comment: new version includes text synthesis by an RNN and experiments with
the COCO benchmar
Annotation Graphs and Servers and Multi-Modal Resources: Infrastructure for Interdisciplinary Education, Research and Development
Annotation graphs and annotation servers offer infrastructure to support the
analysis of human language resources in the form of time-series data such as
text, audio and video. This paper outlines areas of common need among empirical
linguists and computational linguists. After reviewing examples of data and
tools used or under development for each of several areas, it proposes a common
framework for future tool development, data annotation and resource sharing
based upon annotation graphs and servers.Comment: 8 pages, 6 figure
Restructuring multimodal corrective feedback through Augmented Reality (AR)-enabled videoconferencing in L2 pronunciation teaching
The problem of cognitive overload is particularly pertinent in multimedia L2 classroom corrective feedback (CF), which involves rich communicative tools to help the class to notice the mismatch between the target input and learnersâ pronunciation. Based on multimedia design principles, this study developed a new multimodal CF model through augmented reality (AR)-enabled videoconferencing to eliminate extraneous cognitive load and guide learnersâ attention to the essential material. Using a quasi-experimental design, this study aims to examine the effectiveness of this new CF model in improving Chinese L2 studentsâ segmental production and identification of the targeted English consonants (dark /É«/, /Ă°/and /Ξ/), as well as their attitudes towards this application. Results indicated that the online multimodal CF environment equipped with AR annotation and filters played a significant role in improving the participantsâ production of the target segments. However, this advantage was not found in the auditory identification tests compared to the offline CF multimedia class. In addition, the learners reported that the new CF model helped to direct their attention to the articulatory gestures of the student being corrected, and enhance the class efficiency. Implications for computer-assisted pronunciation training and the construction of online/offline multimedia learning environments are also discussed
Semantic Embedding Space for Zero-Shot Action Recognition
The number of categories for action recognition is growing rapidly. It is
thus becoming increasingly hard to collect sufficient training data to learn
conventional models for each category. This issue may be ameliorated by the
increasingly popular 'zero-shot learning' (ZSL) paradigm. In this framework a
mapping is constructed between visual features and a human interpretable
semantic description of each category, allowing categories to be recognised in
the absence of any training data. Existing ZSL studies focus primarily on image
data, and attribute-based semantic representations. In this paper, we address
zero-shot recognition in contemporary video action recognition tasks, using
semantic word vector space as the common space to embed videos and category
labels. This is more challenging because the mapping between the semantic space
and space-time features of videos containing complex actions is more complex
and harder to learn. We demonstrate that a simple self-training and data
augmentation strategy can significantly improve the efficacy of this mapping.
Experiments on human action datasets including HMDB51 and UCF101 demonstrate
that our approach achieves the state-of-the-art zero-shot action recognition
performance.Comment: 5 page
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